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c8d6c8244e
* add yolo cuda preprocessing * cmake build cuda src * yolov5 support cuda preprocessing * yolov5 cuda preprocessing configurable * yolov5 update get mat data api * yolov5 check cuda preprocess args * refactor cuda function name * yolo cuda preprocess padding value configurable * yolov5 release cuda memory * cuda preprocess pybind api update * move use_cuda_preprocessing option to yolov5 model * yolov5lite cuda preprocessing * yolov6 cuda preprocessing * yolov7 cuda preprocessing * yolov7_e2e cuda preprocessing * remove cuda preprocessing in runtime option * refine log and cmake variable name * fix model runtime ptr type Co-authored-by: Jason <jiangjiajun@baidu.com>
74 lines
3.6 KiB
C++
74 lines
3.6 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/pybind/main.h"
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namespace fastdeploy {
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void BindYOLOv5(pybind11::module& m) {
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pybind11::class_<vision::detection::YOLOv5, FastDeployModel>(m, "YOLOv5")
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.def(pybind11::init<std::string, std::string, RuntimeOption,
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ModelFormat>())
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.def("predict",
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[](vision::detection::YOLOv5& self, pybind11::array& data,
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float conf_threshold, float nms_iou_threshold) {
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auto mat = PyArrayToCvMat(data);
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vision::DetectionResult res;
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self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
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return res;
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})
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.def("use_cuda_preprocessing",
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[](vision::detection::YOLOv5& self, int max_image_size) {
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self.UseCudaPreprocessing(max_image_size);
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})
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.def_static("preprocess",
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[](pybind11::array& data, const std::vector<int>& size,
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const std::vector<float> padding_value, bool is_mini_pad,
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bool is_no_pad, bool is_scale_up, int stride, float max_wh,
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bool multi_label) {
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auto mat = PyArrayToCvMat(data);
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fastdeploy::vision::Mat fd_mat(mat);
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FDTensor output;
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std::map<std::string, std::array<float, 2>> im_info;
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vision::detection::YOLOv5::Preprocess(
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&fd_mat, &output, &im_info, size, padding_value,
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is_mini_pad, is_no_pad, is_scale_up, stride, max_wh,
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multi_label);
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return make_pair(TensorToPyArray(output), im_info);
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})
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.def_static(
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"postprocess",
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[](std::vector<pybind11::array> infer_results,
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const std::map<std::string, std::array<float, 2>>& im_info,
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float conf_threshold, float nms_iou_threshold, bool multi_label,
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float max_wh) {
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std::vector<FDTensor> fd_infer_results(infer_results.size());
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PyArrayToTensorList(infer_results, &fd_infer_results, true);
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vision::DetectionResult result;
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vision::detection::YOLOv5::Postprocess(
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fd_infer_results, &result, im_info, conf_threshold,
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nms_iou_threshold, multi_label, max_wh);
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return result;
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})
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.def_readwrite("size", &vision::detection::YOLOv5::size_)
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.def_readwrite("padding_value",
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&vision::detection::YOLOv5::padding_value_)
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.def_readwrite("is_mini_pad", &vision::detection::YOLOv5::is_mini_pad_)
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.def_readwrite("is_no_pad", &vision::detection::YOLOv5::is_no_pad_)
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.def_readwrite("is_scale_up", &vision::detection::YOLOv5::is_scale_up_)
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.def_readwrite("stride", &vision::detection::YOLOv5::stride_)
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.def_readwrite("max_wh", &vision::detection::YOLOv5::max_wh_)
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.def_readwrite("multi_label", &vision::detection::YOLOv5::multi_label_);
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}
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} // namespace fastdeploy
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